The RNA-binding protein hnRNPA2 regulates β-catenin protein expression and is overexpressed in prostate cancer
Bibliographic record
Abstract
INTRODUCTION: The RNA-binding protein hnRNPA2 (HNRNPA2B1) is upregulated in cancer, where it controls alternative pre-mRNA splicing of cancer-relevant genes. Cytoplasmic hnRNPA2 is reported in aggressive cancers, but is functionally uncharacterized. We explored the role of hnRNPA2 in prostate cancer (PCa). METHODS: hnRNPA2 function/localization/expression in PCa was determined using biochemical approaches (colony forming/proliferation/luciferase reporter assays/flow cytometry/immunohistocytochemistry). Binding of hnRNPA2 within cancer-relevant 3'-UTR mRNAs was identified by bioinformatics. RESULTS: RNAi-mediated knockdown of hnRNPA2 reduced colony forming and proliferation, while hnRNPA2 overexpression increased proliferation of PCa cells. Nuclear hnRNPA2 is overexpressed in high-grade clinical PCa, and is also observed in the cytoplasm in some cases. Ectopic expression of a predominantly cytoplasmic variant hnRNPA2-ΔRGG also increased PCa cell proliferation, suggesting that cytoplasmic hnRNPA2 may also be functionally relevant in PCa. Consistent with its known cytoplasmic roles, hnRNPA2 was associated with 3'-UTR mRNAs of several cancer-relevant mRNAs including β-catenin (CTNNB1). Both wild-type hnRNPA2 and hnRNPA2-ΔRGG act on CTNNB1 3'-UTR mRNA, increasing endogenous CTNNB1 mRNA expression and β-catenin protein expression and nuclear localization. CONCLUSION: Nuclear and cytoplasmic hnRNPA2 are present in PCa and appear to be functionally important. Cytoplasmic hnRNPA2 may affect the cancer cell phenotype through 3'-UTR mRNA-mediated regulation of β-catenin expression and other cancer-relevant genes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".